Three categories of cleanup, all zero-behavioral-change:
1. F-strings without placeholders (154 fixes across 29 files)
- Converted f'...' to '...' where no {expression} was present
- Heaviest files: run_agent.py (24), cli.py (20), honcho_integration/cli.py (34)
2. Simplify defensive patterns in run_agent.py
- Added explicit self._is_anthropic_oauth = False in __init__ (before
the api_mode branch that conditionally sets it)
- Replaced 7x getattr(self, '_is_anthropic_oauth', False) with direct
self._is_anthropic_oauth (attribute always initialized now)
- Added _is_openrouter_url() and _is_anthropic_url() helper methods
- Replaced 3 inline 'openrouter' in self._base_url_lower checks
3. Remove dead code in small files
- hermes_cli/claw.py: removed unused 'total' computation
- tools/fuzzy_match.py: removed unused strip_indent() function and
pattern_stripped variable
Full test suite: 6184 passed, 0 failures
E2E PTY: banner clean, tool calls work, zero garbled ANSI
Centralizes two widely-duplicated patterns into hermes_constants.py:
1. get_hermes_home() — Path resolution for ~/.hermes (HERMES_HOME env var)
- Was copy-pasted inline across 30+ files as:
Path(os.getenv("HERMES_HOME", Path.home() / ".hermes"))
- Now defined once in hermes_constants.py (zero-dependency module)
- hermes_cli/config.py re-exports it for backward compatibility
- Removed local wrapper functions in honcho_integration/client.py,
tools/website_policy.py, tools/tirith_security.py, hermes_cli/uninstall.py
2. parse_reasoning_effort() — Reasoning effort string validation
- Was copy-pasted in cli.py, gateway/run.py, cron/scheduler.py
- Same validation logic: check against (xhigh, high, medium, low, minimal, none)
- Now defined once in hermes_constants.py, called from all 3 locations
- Warning log for unknown values kept at call sites (context-specific)
31 files changed, net +31 lines (125 insertions, 94 deletions)
Full test suite: 6179 passed, 0 failed
* fix: respect DashScope v1 runtime mode for alibaba
Remove the hardcoded Alibaba branch from resolve_runtime_provider()
that forced api_mode='anthropic_messages' regardless of the base URL.
Alibaba now goes through the generic API-key provider path, which
auto-detects the protocol from the URL:
- /apps/anthropic → anthropic_messages (via endswith check)
- /v1 → chat_completions (default)
This fixes Alibaba setup with OpenAI-compatible DashScope endpoints
(e.g. coding-intl.dashscope.aliyuncs.com/v1) that were broken because
runtime always forced Anthropic mode even when setup saved a /v1 URL.
Based on PR #2024 by @kshitijk4poor.
* docs(skill): add split, merge, search examples to ocr-and-documents skill
Adds pymupdf examples for PDF splitting, merging, and text search
to the existing ocr-and-documents skill. No new dependencies — pymupdf
already covers all three operations natively.
* fix: replace all production print() calls with logger in rl_training_tool
Replace all bare print() calls in production code paths with proper logger calls.
- Add `import logging` and module-level `logger = logging.getLogger(__name__)`
- Replace print() in _start_training_run() with logger.info()
- Replace print() in _stop_training_run() with logger.info()
- Replace print(Warning/Note) calls with logger.warning() and logger.info()
Using the logging framework allows log level filtering, proper formatting,
and log routing instead of always printing to stdout.
---------
Co-authored-by: kshitijk4poor <kshitijk4poor@users.noreply.github.com>
Co-authored-by: memosr.eth <96793918+memosr@users.noreply.github.com>
MiniMax: Add M2.7 and M2.7-highspeed as new defaults across provider
model lists, auxiliary client, metadata, setup wizard, RL training tool,
fallback tests, and docs. Retain M2.5/M2.1 as alternatives.
OpenRouter: Add grok-4.20-beta, nemotron-3-super-120b-a12b:free,
trinity-large-preview:free, glm-5-turbo, and hunter-alpha to the
model catalog.
MiniMax changes based on PR #1882 by @octo-patch (applied manually
due to stale conflicts in refactored pricing module).
- Add 'emoji' field to ToolEntry and 'get_emoji()' to ToolRegistry
- Add emoji= to all 50+ registry.register() calls across tool files
- Add get_tool_emoji() helper in agent/display.py with 3-tier resolution:
skin override → registry default → hardcoded fallback
- Replace hardcoded emoji maps in run_agent.py, delegate_tool.py, and
gateway/run.py with centralized get_tool_emoji() calls
- Add 'tool_emojis' field to SkinConfig so skins can override per-tool
emojis (e.g. ares skin could use swords instead of wrenches)
- Add 11 tests (5 registry emoji, 6 display/skin integration)
- Update AGENTS.md skin docs table
Based on the approach from PR #1061 by ForgingAlex (emoji centralization
in registry). This salvage fixes several issues from the original:
- Does NOT split the cronjob tool (which would crash on missing schemas)
- Does NOT change image_generate toolset/requires_env/is_async
- Does NOT delete existing tests
- Completes the centralization (gateway/run.py was missed)
- Hooks into the skin system for full customizability
The tinker-atropos submodule and its heavy dependencies (atroposlib, tinker,
wandb, fastapi, uvicorn) were being installed for all users by default,
adding significant install time and disk usage for most users who don't
need RL training capabilities.
Changes:
- install.sh: Only init mini-swe-agent submodule by default; skip
tinker-atropos clone and install entirely
- install.sh: Remove --recurse-submodules from git clone (only fetches
what's needed)
- pyproject.toml: Add [rl] optional dependency group for explicit opt-in
- rl_training_tool.py: Move LOGS_DIR.mkdir() from module-level to lazy
init (_ensure_logs_dir) to avoid side effects on import
- README.md: Update contributor quick start to not auto-fetch
tinker-atropos; add RL opt-in instructions
Users who want RL training can opt in with:
git submodule update --init tinker-atropos
uv pip install -e ./tinker-atropos
- Add `agent`, `tools.*`, `gateway.*` to packages.find include
- Add `hermes_state`, `hermes_time`, `mini_swe_runner`, `rl_cli`, `utils` to py-modules
- Move rl_training_tool LOGS_DIR to ~/.hermes/logs/rl_training/ (was writing
into the package source tree, which fails on read-only installs)
These were masked in development (editable installs see the whole source tree)
but broke any non-editable install like `pip install .` or wheel builds.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The 4 early-return paths in _spawn_training_run (API exit, trainer
exit, env not found, env exit) were doing manual process.terminate()
or returning without cleanup, leaking open log file handles. Now all
paths call _stop_training_run() which handles both process termination
and file handle closure.
Also adds 12 tests for _stop_training_run covering file handle
cleanup, process termination, status transitions, and edge cases.
Inspired by PR #715 (0xbyt4) which identified the early-return issue.
Core file handle fix was already on main via e28dc13 (memosr.eth).
- Added `prompt_toolkit` as a direct dependency for interactive CLI support.
- Updated `modal` optional dependency to require `swe-rex[modal]>=1.4.0` for improved cloud execution capabilities.
- Enhanced `messaging` optional dependencies to include `aiohttp>=3.9.0` for WhatsApp bridge communication.
- Refined installation scripts to check for Python version requirements, emphasizing the need for Python 3.11+ for RL training tools.
- Improved setup scripts to ensure proper installation of submodules and dependencies, enhancing user experience during setup.
- Added unique run ID generation for WandB tracking during test inference.
- Enabled WandB usage for test tracking and updated command-line arguments accordingly.
- Implemented real-time output streaming for process execution, improving log visibility and debugging.
- Enhanced error handling to display last few lines of stderr for better troubleshooting.
- Modified `model_tools.py` to update default model IDs and add new RL function `rl_test_inference`.
- Enhanced `README.md` with installation instructions for submodules and updated API key usage.
- Improved `rl_cli.py` to load configuration from `~/.hermes/config.yaml` and set terminal working directory for RL tools.
- Updated `run_agent.py` to handle empty string arguments as empty objects for better JSON validation.
- Refined installation scripts to ensure submodules are cloned and installed correctly, enhancing setup experience.
- Added the tinker-atropos submodule for enhanced RL training capabilities.
- Updated model_tools.py to reorder RL function definitions and improve descriptions.
- Modified rl_cli.py to include checks for the tinker-atropos setup and provide user guidance.
- Adjusted toolsets.py and __init__.py to reflect changes in RL function availability.
- Enhanced rl_training_tool.py to manage training processes directly without a separate API server.
- Updated `.env.example` to include Tinker and WandB API keys for reinforcement learning training.
- Enhanced `model_tools.py` to clarify configuration options and streamline the RL training process.
- Expanded `README.md` with detailed instructions for setting up RL training using Tinker and WandB.
- Modified `hermes_cli` files to integrate RL training tools and ensure proper configuration checks.
- Improved `rl_training_tool.py` to reflect changes in training parameters and configuration management.
